Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The hypothalamic-based kisspeptin-signaling system is a major positive regulator of the neuroendocrine-reproductive axis in mammals. During the last decade, major advances have been made in understanding how this signaling system is regulated and how it can be manipulated clinically to achieve beneficial outcomes in treating sex steroid-dependent disorders. Interestingly, kisspeptin was not first identified as a regulator of fertility. Instead, approximately 7 years earlier KISS1 was reported to be expressed in nonmetastatic melanoma cells and was subsequently demonstrated to act as a powerful suppressor of the metastatic potential of malignant melanoma cells. Since this discovery, numerous studies have demonstrated the expression of the kisspeptin-signaling system at several peripheral sites implicating it in biological processes such as the regulation of ovarian function, embryo implantation, placentation, angiogenesis, insulin secretion, and kidney development. Although much work remains to be done to assess how important kisspeptin signaling is in regulating some of these processes, for other processes recent studies have made tremendous strides toward such an assessment. Using mice lacking either Kiss1 or Kiss1r alleles, researchers have provided compelling evidence for kisspeptin playing a major role in regulating breast cancer metastasis, oocyte survival, follicular maturation, ovulation, and embryo implantation. This review critically discusses the findings from these as well as other studies which suggest roles for kisspeptin in regulating important physiological processes beyond the brain. It also discusses the challenges that lie ahead in determining whether findings made with animal models are relevant in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it